Overview

Dataset statistics

Number of variables18
Number of observations1529
Missing cells577
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory802.5 KiB
Average record size in memory537.4 B

Variable types

Numeric12
Categorical6

Warnings

DataYear has constant value "2015" Constant
LargestPropertyUseType has a high cardinality: 56 distinct values High cardinality
Location has a high cardinality: 1493 distinct values High cardinality
LargestPropertyUseType is highly correlated with DataYearHigh correlation
NumberofBuildings is highly correlated with DataYearHigh correlation
DataYear is highly correlated with LargestPropertyUseType and 3 other fieldsHigh correlation
Neighborhood is highly correlated with DataYearHigh correlation
PrimaryPropertyType is highly correlated with DataYearHigh correlation
LargestPropertyUseType has 61 (4.0%) missing values Missing
ENERGYSTARScore has 507 (33.2%) missing values Missing
Location is uniformly distributed Uniform
OSEBuildingID has unique values Unique
PropertyGFAParking has 1187 (77.6%) zeros Zeros

Reproduction

Analysis started2021-02-23 15:17:11.786495
Analysis finished2021-02-23 15:17:42.699360
Duration30.91 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

OSEBuildingID
Real number (ℝ≥0)

UNIQUE

Distinct1529
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15756.91629
Minimum1
Maximum50038
Zeros0
Zeros (%)0.0%
Memory size12.1 KiB
2021-02-23T16:17:42.827486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile133.6
Q1602
median21140
Q324514
95-th percentile28320
Maximum50038
Range50037
Interquartile range (IQR)23912

Descriptive statistics

Standard deviation12924.61682
Coefficient of variation (CV)0.8202503958
Kurtosis-0.6050413123
Mean15756.91629
Median Absolute Deviation (MAD)5460
Skewness0.1435026679
Sum24092325
Variance167045719.9
MonotocityStrictly increasing
2021-02-23T16:17:43.008650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
217231
 
0.1%
212191
 
0.1%
278821
 
0.1%
250011
 
0.1%
225041
 
0.1%
255431
 
0.1%
212331
 
0.1%
239511
 
0.1%
239281
 
0.1%
Other values (1519)1519
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
51
0.1%
81
0.1%
91
0.1%
101
0.1%
111
0.1%
121
0.1%
151
0.1%
ValueCountFrequency (%)
500381
0.1%
500021
0.1%
499981
0.1%
499851
0.1%
499661
0.1%
499581
0.1%
499461
0.1%
499451
0.1%
499401
0.1%
499261
0.1%

DataYear
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size91.2 KiB
2015
1529 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters6116
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015
ValueCountFrequency (%)
20151529
100.0%
2021-02-23T16:17:43.326931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T16:17:43.429024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
20151529
100.0%

Most occurring characters

ValueCountFrequency (%)
21529
25.0%
01529
25.0%
11529
25.0%
51529
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6116
100.0%

Most frequent character per category

ValueCountFrequency (%)
21529
25.0%
01529
25.0%
11529
25.0%
51529
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common6116
100.0%

Most frequent character per script

ValueCountFrequency (%)
21529
25.0%
01529
25.0%
11529
25.0%
51529
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6116
100.0%

Most frequent character per block

ValueCountFrequency (%)
21529
25.0%
01529
25.0%
11529
25.0%
51529
25.0%

PrimaryPropertyType
Categorical

HIGH CORRELATION

Distinct24
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size110.5 KiB
Small- and Mid-Sized Office
296 
Other
243 
Non-Refrigerated Warehouse
187 
Large Office
170 
Mixed Use Property
103 
Other values (19)
530 

Length

Max length27
Median length18
Mean length16.90451275
Min length5

Characters and Unicode

Total characters25847
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowHotel
ValueCountFrequency (%)
Small- and Mid-Sized Office296
19.4%
Other243
15.9%
Non-Refrigerated Warehouse187
12.2%
Large Office170
11.1%
Mixed Use Property103
 
6.7%
Retail Store100
 
6.5%
Hotel73
 
4.8%
Worship Facility72
 
4.7%
Distribution Center 51
 
3.3%
Medical Office43
 
2.8%
Other values (14)191
12.5%
2021-02-23T16:17:43.724292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
office509
14.9%
small296
 
8.7%
and296
 
8.7%
mid-sized296
 
8.7%
other243
 
7.1%
warehouse200
 
5.9%
non-refrigerated187
 
5.5%
large170
 
5.0%
store136
 
4.0%
mixed103
 
3.0%
Other values (25)982
28.7%

Most occurring characters

ValueCountFrequency (%)
e3292
 
12.7%
i2091
 
8.1%
1889
 
7.3%
r1802
 
7.0%
a1538
 
6.0%
t1262
 
4.9%
d1249
 
4.8%
f1247
 
4.8%
o1063
 
4.1%
l1049
 
4.1%
Other values (35)9365
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter19177
74.2%
Uppercase Letter3701
 
14.3%
Space Separator1889
 
7.3%
Dash Punctuation847
 
3.3%
Control88
 
0.3%
Decimal Number78
 
0.3%
Other Punctuation67
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
e3292
17.2%
i2091
10.9%
r1802
9.4%
a1538
8.0%
t1262
 
6.6%
d1249
 
6.5%
f1247
 
6.5%
o1063
 
5.5%
l1049
 
5.5%
c742
 
3.9%
Other values (14)3842
20.0%
ValueCountFrequency (%)
S878
23.7%
O752
20.3%
M443
12.0%
R327
 
8.8%
W272
 
7.3%
N187
 
5.1%
L172
 
4.6%
U119
 
3.2%
C107
 
2.9%
P103
 
2.8%
Other values (5)341
 
9.2%
ValueCountFrequency (%)
139
50.0%
239
50.0%
ValueCountFrequency (%)
1889
100.0%
ValueCountFrequency (%)
/67
100.0%
ValueCountFrequency (%)
-847
100.0%
ValueCountFrequency (%)
88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22878
88.5%
Common2969
 
11.5%

Most frequent character per script

ValueCountFrequency (%)
e3292
14.4%
i2091
 
9.1%
r1802
 
7.9%
a1538
 
6.7%
t1262
 
5.5%
d1249
 
5.5%
f1247
 
5.5%
o1063
 
4.6%
l1049
 
4.6%
S878
 
3.8%
Other values (29)7407
32.4%
ValueCountFrequency (%)
1889
63.6%
-847
28.5%
88
 
3.0%
/67
 
2.3%
139
 
1.3%
239
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII25847
100.0%

Most frequent character per block

ValueCountFrequency (%)
e3292
 
12.7%
i2091
 
8.1%
1889
 
7.3%
r1802
 
7.0%
a1538
 
6.0%
t1262
 
4.9%
d1249
 
4.8%
f1247
 
4.8%
o1063
 
4.1%
l1049
 
4.1%
Other values (35)9365
36.2%

YearBuilt
Real number (ℝ≥0)

Distinct112
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1960.390451
Minimum1900
Maximum2014
Zeros0
Zeros (%)0.0%
Memory size12.1 KiB
2021-02-23T16:17:43.880434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1906
Q11929
median1965
Q31988
95-th percentile2008
Maximum2014
Range114
Interquartile range (IQR)59

Descriptive statistics

Standard deviation32.87203394
Coefficient of variation (CV)0.01676810552
Kurtosis-1.095019394
Mean1960.390451
Median Absolute Deviation (MAD)27
Skewness-0.2630421728
Sum2997437
Variance1080.570616
MonotocityNot monotonic
2021-02-23T16:17:44.057604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190045
 
2.9%
197930
 
2.0%
200030
 
2.0%
191030
 
2.0%
196028
 
1.8%
197028
 
1.8%
192627
 
1.8%
196226
 
1.7%
192826
 
1.7%
200825
 
1.6%
Other values (102)1234
80.7%
ValueCountFrequency (%)
190045
2.9%
19012
 
0.1%
19029
 
0.6%
19033
 
0.2%
190413
 
0.9%
19054
 
0.3%
190614
 
0.9%
190712
 
0.8%
190810
 
0.7%
190915
 
1.0%
ValueCountFrequency (%)
20149
 
0.6%
201313
0.9%
20126
 
0.4%
20112
 
0.1%
20106
 
0.4%
200923
1.5%
200825
1.6%
20079
 
0.6%
200616
1.0%
200513
0.9%

NumberofBuildings
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size86.7 KiB
1
1524 
7
 
2
3
 
1
2
 
1
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1529
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
11524
99.7%
72
 
0.1%
31
 
0.1%
21
 
0.1%
61
 
0.1%
2021-02-23T16:17:44.428932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-23T16:17:44.535029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
11524
99.7%
72
 
0.1%
31
 
0.1%
21
 
0.1%
61
 
0.1%

Most occurring characters

ValueCountFrequency (%)
11524
99.7%
72
 
0.1%
31
 
0.1%
21
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1529
100.0%

Most frequent character per category

ValueCountFrequency (%)
11524
99.7%
72
 
0.1%
31
 
0.1%
21
 
0.1%
61
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1529
100.0%

Most frequent character per script

ValueCountFrequency (%)
11524
99.7%
72
 
0.1%
31
 
0.1%
21
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1529
100.0%

Most frequent character per block

ValueCountFrequency (%)
11524
99.7%
72
 
0.1%
31
 
0.1%
21
 
0.1%
61
 
0.1%

NumberofFloors
Real number (ℝ≥0)

Distinct45
Distinct (%)3.0%
Missing7
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean4.29303548
Minimum0
Maximum99
Zeros5
Zeros (%)0.3%
Memory size12.1 KiB
2021-02-23T16:17:44.711189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile13.95
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.794112496
Coefficient of variation (CV)1.582589412
Kurtosis49.58480487
Mean4.29303548
Median Absolute Deviation (MAD)1
Skewness5.884680254
Sum6534
Variance46.15996461
MonotocityNot monotonic
2021-02-23T16:17:44.892353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1412
26.9%
2350
22.9%
3244
16.0%
4143
 
9.4%
599
 
6.5%
683
 
5.4%
733
 
2.2%
821
 
1.4%
1118
 
1.2%
1016
 
1.0%
Other values (35)103
 
6.7%
ValueCountFrequency (%)
05
 
0.3%
1412
26.9%
2350
22.9%
3244
16.0%
4143
 
9.4%
599
 
6.5%
683
 
5.4%
733
 
2.2%
821
 
1.4%
98
 
0.5%
ValueCountFrequency (%)
991
 
0.1%
761
 
0.1%
631
 
0.1%
561
 
0.1%
551
 
0.1%
491
 
0.1%
471
 
0.1%
461
 
0.1%
425
0.3%
412
 
0.1%

PropertyGFABuilding(s)
Real number (ℝ)

Distinct1447
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96319.22629
Minimum-50550
Maximum1765970
Zeros0
Zeros (%)0.0%
Memory size12.1 KiB
2021-02-23T16:17:45.078532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-50550
5-th percentile21027.6
Q127788
median45082
Q390266
95-th percentile332731.2
Maximum1765970
Range1816520
Interquartile range (IQR)62478

Descriptive statistics

Standard deviation164287.3499
Coefficient of variation (CV)1.705654792
Kurtosis31.97702714
Mean96319.22629
Median Absolute Deviation (MAD)20882
Skewness5.051704685
Sum147272097
Variance2.699033333 × 1010
MonotocityNot monotonic
2021-02-23T16:17:45.264701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
216008
 
0.5%
288007
 
0.5%
259207
 
0.5%
360006
 
0.4%
240005
 
0.3%
333003
 
0.2%
223882
 
0.1%
567002
 
0.1%
278002
 
0.1%
380382
 
0.1%
Other values (1437)1485
97.1%
ValueCountFrequency (%)
-505501
0.1%
-433101
0.1%
109251
0.1%
128061
0.1%
150001
0.1%
162001
0.1%
178241
0.1%
179561
0.1%
183961
0.1%
191931
0.1%
ValueCountFrequency (%)
17659701
0.1%
16328201
0.1%
14000001
0.1%
13809591
0.1%
13230551
0.1%
12954571
0.1%
12582801
0.1%
12157181
0.1%
11721271
0.1%
11586911
0.1%

PropertyGFAParking
Real number (ℝ)

ZEROS

Distinct335
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14330.77436
Minimum-2
Maximum512608
Zeros1187
Zeros (%)77.6%
Memory size12.1 KiB
2021-02-23T16:17:45.466876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q10
median0
Q30
95-th percentile98093.6
Maximum512608
Range512610
Interquartile range (IQR)0

Descriptive statistics

Standard deviation45245.70688
Coefficient of variation (CV)3.157240895
Kurtosis34.20368265
Mean14330.77436
Median Absolute Deviation (MAD)0
Skewness5.15909704
Sum21911754
Variance2047173991
MonotocityNot monotonic
2021-02-23T16:17:45.871243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01187
77.6%
133203
 
0.2%
259202
 
0.1%
258002
 
0.1%
1001762
 
0.1%
204162
 
0.1%
300002
 
0.1%
108002
 
0.1%
1242161
 
0.1%
525821
 
0.1%
Other values (325)325
 
21.3%
ValueCountFrequency (%)
-21
 
0.1%
01187
77.6%
12631
 
0.1%
13921
 
0.1%
22111
 
0.1%
23521
 
0.1%
37641
 
0.1%
38341
 
0.1%
42561
 
0.1%
45531
 
0.1%
ValueCountFrequency (%)
5126081
0.1%
4401851
0.1%
4077951
0.1%
3898601
0.1%
3689801
0.1%
3351091
0.1%
3276801
0.1%
3194001
0.1%
3037071
0.1%
2974571
0.1%

LargestPropertyUseType
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct56
Distinct (%)3.8%
Missing61
Missing (%)4.0%
Memory size103.2 KiB
Office
477 
Non-Refrigerated Warehouse
194 
Retail Store
97 
Other
92 
Worship Facility
70 
Other values (51)
538 

Length

Max length52
Median length11
Mean length13.60490463
Min length5

Characters and Unicode

Total characters19972
Distinct characters51
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.7%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowHotel
ValueCountFrequency (%)
Office477
31.2%
Non-Refrigerated Warehouse194
12.7%
Retail Store97
 
6.3%
Other92
 
6.0%
Worship Facility70
 
4.6%
Hotel68
 
4.4%
Distribution Center52
 
3.4%
Medical Office43
 
2.8%
K-12 School39
 
2.6%
Supermarket/Grocery Store37
 
2.4%
Other values (46)299
19.6%
(Missing)61
 
4.0%
2021-02-23T16:17:46.277612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
office523
21.8%
warehouse206
 
8.6%
non-refrigerated194
 
8.1%
other155
 
6.5%
store134
 
5.6%
facility98
 
4.1%
retail97
 
4.0%
72
 
3.0%
worship70
 
2.9%
hotel68
 
2.8%
Other values (80)779
32.5%

Most occurring characters

ValueCountFrequency (%)
e2788
14.0%
i1695
 
8.5%
r1562
 
7.8%
t1302
 
6.5%
f1300
 
6.5%
o1078
 
5.4%
a1039
 
5.2%
928
 
4.6%
c893
 
4.5%
O691
 
3.5%
Other values (41)6696
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15772
79.0%
Uppercase Letter2673
 
13.4%
Space Separator928
 
4.6%
Dash Punctuation325
 
1.6%
Other Punctuation164
 
0.8%
Decimal Number78
 
0.4%
Open Punctuation16
 
0.1%
Close Punctuation16
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
O691
25.9%
R365
13.7%
S336
12.6%
W277
10.4%
N194
 
7.3%
C131
 
4.9%
H122
 
4.6%
F107
 
4.0%
M92
 
3.4%
D79
 
3.0%
Other values (11)279
10.4%
ValueCountFrequency (%)
e2788
17.7%
i1695
10.7%
r1562
9.9%
t1302
8.3%
f1300
8.2%
o1078
 
6.8%
a1039
 
6.6%
c893
 
5.7%
l654
 
4.1%
n605
 
3.8%
Other values (11)2856
18.1%
ValueCountFrequency (%)
/135
82.3%
,20
 
12.2%
&9
 
5.5%
ValueCountFrequency (%)
139
50.0%
239
50.0%
ValueCountFrequency (%)
928
100.0%
ValueCountFrequency (%)
-325
100.0%
ValueCountFrequency (%)
(16
100.0%
ValueCountFrequency (%)
)16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18445
92.4%
Common1527
 
7.6%

Most frequent character per script

ValueCountFrequency (%)
e2788
15.1%
i1695
 
9.2%
r1562
 
8.5%
t1302
 
7.1%
f1300
 
7.0%
o1078
 
5.8%
a1039
 
5.6%
c893
 
4.8%
O691
 
3.7%
l654
 
3.5%
Other values (32)5443
29.5%
ValueCountFrequency (%)
928
60.8%
-325
 
21.3%
/135
 
8.8%
139
 
2.6%
239
 
2.6%
,20
 
1.3%
(16
 
1.0%
)16
 
1.0%
&9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII19972
100.0%

Most frequent character per block

ValueCountFrequency (%)
e2788
14.0%
i1695
 
8.5%
r1562
 
7.8%
t1302
 
6.5%
f1300
 
6.5%
o1078
 
5.4%
a1039
 
5.2%
928
 
4.6%
c893
 
4.5%
O691
 
3.5%
Other values (41)6696
33.5%

ENERGYSTARScore
Real number (ℝ≥0)

MISSING

Distinct100
Distinct (%)9.8%
Missing507
Missing (%)33.2%
Infinite0
Infinite (%)0.0%
Mean62.37475538
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Memory size12.1 KiB
2021-02-23T16:17:46.452781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q143
median69
Q386
95-th percentile98
Maximum100
Range99
Interquartile range (IQR)43

Descriptive statistics

Standard deviation29.06437633
Coefficient of variation (CV)0.4659637725
Kurtosis-0.7631450277
Mean62.37475538
Median Absolute Deviation (MAD)20
Skewness-0.6175145488
Sum63747
Variance844.7379713
MonotocityNot monotonic
2021-02-23T16:17:46.629933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10031
 
2.0%
126
 
1.7%
9525
 
1.6%
8124
 
1.6%
8923
 
1.5%
8621
 
1.4%
9121
 
1.4%
9320
 
1.3%
7720
 
1.3%
9820
 
1.3%
Other values (90)791
51.7%
(Missing)507
33.2%
ValueCountFrequency (%)
126
1.7%
27
 
0.5%
36
 
0.4%
49
 
0.6%
52
 
0.1%
64
 
0.3%
76
 
0.4%
810
 
0.7%
93
 
0.2%
102
 
0.1%
ValueCountFrequency (%)
10031
2.0%
9919
1.2%
9820
1.3%
9719
1.2%
966
 
0.4%
9525
1.6%
9419
1.2%
9320
1.3%
9218
1.2%
9121
1.4%

SiteEnergyUse(kBtu)
Real number (ℝ≥0)

Distinct1526
Distinct (%)99.9%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7553921.257
Minimum0
Maximum295812640
Zeros2
Zeros (%)0.1%
Memory size12.1 KiB
2021-02-23T16:17:46.808095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile400782.7
Q11149832
median2508795
Q36994638.5
95-th percentile28845309.4
Maximum295812640
Range295812640
Interquartile range (IQR)5844806.5

Descriptive statistics

Standard deviation18537172.75
Coefficient of variation (CV)2.453980141
Kurtosis126.6082797
Mean7553921.257
Median Absolute Deviation (MAD)1749527.5
Skewness9.535819006
Sum1.154239168 × 1010
Variance3.436267736 × 1014
MonotocityNot monotonic
2021-02-23T16:17:46.980766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02
 
0.1%
20741522
 
0.1%
27219541
 
0.1%
132539791
 
0.1%
115118801
 
0.1%
110269451
 
0.1%
22021141
 
0.1%
15256241
 
0.1%
18202921
 
0.1%
14560391
 
0.1%
Other values (1516)1516
99.1%
ValueCountFrequency (%)
02
0.1%
114411
0.1%
171501
0.1%
241261
0.1%
439431
0.1%
534011
0.1%
564931
0.1%
828241
0.1%
919961
0.1%
938021
0.1%
ValueCountFrequency (%)
2958126401
0.1%
2866855361
0.1%
2848671681
0.1%
2511918241
0.1%
1376356961
0.1%
1049772481
0.1%
945600881
0.1%
941786481
0.1%
853579521
0.1%
849807601
0.1%

TotalGHGEmissions
Real number (ℝ≥0)

Distinct1462
Distinct (%)95.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean162.7709097
Minimum0
Maximum11824.89
Zeros2
Zeros (%)0.1%
Memory size12.1 KiB
2021-02-23T16:17:47.165925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.6905
Q118.4925
median46.92
Q3135.0325
95-th percentile564.9955
Maximum11824.89
Range11824.89
Interquartile range (IQR)116.54

Descriptive statistics

Standard deviation557.2669998
Coefficient of variation (CV)3.423627728
Kurtosis243.3293813
Mean162.7709097
Median Absolute Deviation (MAD)35.83
Skewness13.6927156
Sum248713.95
Variance310546.509
MonotocityNot monotonic
2021-02-23T16:17:47.343086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.193
 
0.2%
6.713
 
0.2%
02
 
0.1%
4.622
 
0.1%
19.952
 
0.1%
3.312
 
0.1%
12.712
 
0.1%
42.412
 
0.1%
48.62
 
0.1%
29.262
 
0.1%
Other values (1452)1506
98.5%
ValueCountFrequency (%)
02
0.1%
0.081
0.1%
0.121
0.1%
0.171
0.1%
0.311
0.1%
0.351
0.1%
0.371
0.1%
0.641
0.1%
0.651
0.1%
0.661
0.1%
ValueCountFrequency (%)
11824.891
0.1%
10780.641
0.1%
8046.71
0.1%
4725.431
0.1%
3894.011
0.1%
3321.021
0.1%
3044.631
0.1%
2937.831
0.1%
2846.071
0.1%
2452.861
0.1%

Location
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1493
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size320.6 KiB
{'latitude': '47.66375728', 'longitude': '-122.3002168', 'human_address': '{"address": "2623 NE UNIVERSITY VILLAGE ST", "city": "SEATTLE", "state": "WA", "zip": "98105"}'}
 
5
{'latitude': '47.52593209', 'longitude': '-122.3308402', 'human_address': '{"address": "309 S CLOVERDALE ST", "city": "SEATTLE", "state": "WA", "zip": "98108"}'}
 
5
{'latitude': '47.52131741', 'longitude': '-122.3668974', 'human_address': '{"address": "2600 SW BARTON ST", "city": "SEATTLE", "state": "WA", "zip": "98126"}'}
 
4
{'latitude': '47.5829049', 'longitude': '-122.3228994', 'human_address': '{"address": "2203 AIRPORT WAY S", "city": "SEATTLE", "state": "WA", "zip": "98134"}'}
 
4
{'latitude': '47.5616226', 'longitude': '-122.3386303', 'human_address': '{"address": "4634 E MARGINAL WAY S", "city": "SEATTLE", "state": "WA", "zip": "98134"}'}
 
3
Other values (1488)
1508 

Length

Max length176
Median length157
Mean length157.6357096
Min length151

Characters and Unicode

Total characters241025
Distinct characters63
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1472 ?
Unique (%)96.3%

Sample

1st row{'latitude': '47.61219025', 'longitude': '-122.33799744', 'human_address': '{"address": "405 OLIVE WAY", "city": "SEATTLE", "state": "WA", "zip": "98101"}'}
2nd row{'latitude': '47.61310583', 'longitude': '-122.33335756', 'human_address': '{"address": "724 PINE ST", "city": "SEATTLE", "state": "WA", "zip": "98101"}'}
3rd row{'latitude': '47.61334897', 'longitude': '-122.33769944', 'human_address': '{"address": "1900 5TH AVE", "city": "SEATTLE", "state": "WA", "zip": "98101"}'}
4th row{'latitude': '47.61421585', 'longitude': '-122.33660889', 'human_address': '{"address": "620 STEWART ST", "city": "SEATTLE", "state": "WA", "zip": "98101"}'}
5th row{'latitude': '47.6137544', 'longitude': '-122.3409238', 'human_address': '{"address": "401 LENORA ST", "city": "SEATTLE", "state": "WA", "zip": "98121"}'}
ValueCountFrequency (%)
{'latitude': '47.66375728', 'longitude': '-122.3002168', 'human_address': '{"address": "2623 NE UNIVERSITY VILLAGE ST", "city": "SEATTLE", "state": "WA", "zip": "98105"}'}5
 
0.3%
{'latitude': '47.52593209', 'longitude': '-122.3308402', 'human_address': '{"address": "309 S CLOVERDALE ST", "city": "SEATTLE", "state": "WA", "zip": "98108"}'}5
 
0.3%
{'latitude': '47.52131741', 'longitude': '-122.3668974', 'human_address': '{"address": "2600 SW BARTON ST", "city": "SEATTLE", "state": "WA", "zip": "98126"}'}4
 
0.3%
{'latitude': '47.5829049', 'longitude': '-122.3228994', 'human_address': '{"address": "2203 AIRPORT WAY S", "city": "SEATTLE", "state": "WA", "zip": "98134"}'}4
 
0.3%
{'latitude': '47.5616226', 'longitude': '-122.3386303', 'human_address': '{"address": "4634 E MARGINAL WAY S", "city": "SEATTLE", "state": "WA", "zip": "98134"}'}3
 
0.2%
{'latitude': '47.62124083', 'longitude': '-122.3534322', 'human_address': '{"address": "305 HARRISON ST", "city": "SEATTLE", "state": "WA", "zip": "98109"}'}3
 
0.2%
{'latitude': '47.5309583', 'longitude': '-122.3320685', 'human_address': '{"address": "121 S KENYON ST", "city": "SEATTLE", "state": "WA", "zip": "98108"}'}3
 
0.2%
{'latitude': '47.64171214', 'longitude': '-122.3173859', 'human_address': '{"address": "2400 11TH AVE E", "city": "SEATTLE", "state": "WA", "zip": "98102"}'}3
 
0.2%
{'latitude': '47.60387657', 'longitude': '-122.33374327', 'human_address': '{"address": "818 2ND AVE", "city": "SEATTLE", "state": "WA", "zip": "98104"}'}3
 
0.2%
{'latitude': '47.59845416', 'longitude': '-122.300978', 'human_address': '{"address": "2309 S JACKSON ST", "city": "SEATTLE", "state": "WA", "zip": "98144"}'}2
 
0.1%
Other values (1483)1494
97.7%
2021-02-23T16:17:47.774480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city1542
 
6.4%
zip1529
 
6.3%
longitude1529
 
6.3%
wa1529
 
6.3%
address1529
 
6.3%
seattle1529
 
6.3%
human_address1529
 
6.3%
state1529
 
6.3%
latitude1529
 
6.3%
ave868
 
3.6%
Other values (4178)9501
39.4%

Most occurring characters

ValueCountFrequency (%)
"24464
 
10.1%
22614
 
9.4%
'18348
 
7.6%
:10703
 
4.4%
t9174
 
3.8%
d9174
 
3.8%
a7645
 
3.2%
e7645
 
3.2%
,7645
 
3.2%
s7645
 
3.2%
Other values (53)115968
48.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter73392
30.4%
Other Punctuation64218
26.6%
Decimal Number44817
18.6%
Uppercase Letter26810
 
11.1%
Space Separator22614
 
9.4%
Open Punctuation3058
 
1.3%
Close Punctuation3058
 
1.3%
Dash Punctuation1529
 
0.6%
Connector Punctuation1529
 
0.6%

Most frequent character per category

ValueCountFrequency (%)
E4908
18.3%
A4820
18.0%
T4401
16.4%
S2880
10.7%
W2065
7.7%
L1943
 
7.2%
N1013
 
3.8%
V976
 
3.6%
R712
 
2.7%
H530
 
2.0%
Other values (15)2562
9.6%
ValueCountFrequency (%)
t9174
12.5%
d9174
12.5%
a7645
10.4%
e7645
10.4%
s7645
10.4%
i6116
8.3%
u4587
 
6.2%
l3058
 
4.2%
n3058
 
4.2%
r3058
 
4.2%
Other values (8)12232
16.7%
ValueCountFrequency (%)
16991
15.6%
26618
14.8%
44635
10.3%
34516
10.1%
03880
8.7%
73879
8.7%
93851
8.6%
83846
8.6%
63429
7.7%
53172
7.1%
ValueCountFrequency (%)
"24464
38.1%
'18348
28.6%
:10703
16.7%
,7645
 
11.9%
.3058
 
4.8%
ValueCountFrequency (%)
{3058
100.0%
ValueCountFrequency (%)
22614
100.0%
ValueCountFrequency (%)
-1529
100.0%
ValueCountFrequency (%)
_1529
100.0%
ValueCountFrequency (%)
}3058
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common140823
58.4%
Latin100202
41.6%

Most frequent character per script

ValueCountFrequency (%)
t9174
 
9.2%
d9174
 
9.2%
a7645
 
7.6%
e7645
 
7.6%
s7645
 
7.6%
i6116
 
6.1%
E4908
 
4.9%
A4820
 
4.8%
u4587
 
4.6%
T4401
 
4.4%
Other values (33)34087
34.0%
ValueCountFrequency (%)
"24464
17.4%
22614
16.1%
'18348
13.0%
:10703
 
7.6%
,7645
 
5.4%
16991
 
5.0%
26618
 
4.7%
44635
 
3.3%
34516
 
3.2%
03880
 
2.8%
Other values (10)30409
21.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII241025
100.0%

Most frequent character per block

ValueCountFrequency (%)
"24464
 
10.1%
22614
 
9.4%
'18348
 
7.6%
:10703
 
4.4%
t9174
 
3.8%
d9174
 
3.8%
a7645
 
3.2%
e7645
 
3.2%
,7645
 
3.2%
s7645
 
3.2%
Other values (53)115968
48.1%

CouncilDistrictCode
Real number (ℝ≥0)

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.431000654
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size12.1 KiB
2021-02-23T16:17:47.912604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.202296898
Coefficient of variation (CV)0.4970202152
Kurtosis-1.604539498
Mean4.431000654
Median Absolute Deviation (MAD)2
Skewness-0.03483576817
Sum6775
Variance4.850111629
MonotocityNot monotonic
2021-02-23T16:17:48.025716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7517
33.8%
2367
24.0%
3184
 
12.0%
4147
 
9.6%
5117
 
7.7%
6100
 
6.5%
197
 
6.3%
ValueCountFrequency (%)
197
 
6.3%
2367
24.0%
3184
 
12.0%
4147
 
9.6%
5117
 
7.7%
6100
 
6.5%
7517
33.8%
ValueCountFrequency (%)
7517
33.8%
6100
 
6.5%
5117
 
7.7%
4147
 
9.6%
3184
 
12.0%
2367
24.0%
197
 
6.3%

Neighborhood
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size101.3 KiB
DOWNTOWN
361 
GREATER DUWAMISH
324 
LAKE UNION
142 
MAGNOLIA / QUEEN ANNE
140 
EAST
115 
Other values (8)
447 

Length

Max length21
Median length9
Mean length10.75212557
Min length4

Characters and Unicode

Total characters16440
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDOWNTOWN
2nd rowDOWNTOWN
3rd rowDOWNTOWN
4th rowDOWNTOWN
5th rowDOWNTOWN
ValueCountFrequency (%)
DOWNTOWN361
23.6%
GREATER DUWAMISH324
21.2%
LAKE UNION142
 
9.3%
MAGNOLIA / QUEEN ANNE140
 
9.2%
EAST115
 
7.5%
NORTHEAST106
 
6.9%
NORTHWEST77
 
5.0%
BALLARD61
 
4.0%
NORTH57
 
3.7%
CENTRAL44
 
2.9%
Other values (3)102
 
6.7%
2021-02-23T16:17:48.354005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
downtown361
14.9%
duwamish324
13.4%
greater324
13.4%
lake142
 
5.9%
union142
 
5.9%
magnolia140
 
5.8%
anne140
 
5.8%
140
 
5.8%
queen140
 
5.8%
east115
 
4.8%
Other values (8)447
18.5%

Most occurring characters

ValueCountFrequency (%)
N1850
11.3%
E1691
10.3%
A1629
9.9%
T1397
 
8.5%
O1309
 
8.0%
W1156
 
7.0%
R1030
 
6.3%
886
 
5.4%
D820
 
5.0%
S752
 
4.6%
Other values (11)3920
23.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter15414
93.8%
Space Separator886
 
5.4%
Other Punctuation140
 
0.9%

Most frequent character per category

ValueCountFrequency (%)
N1850
12.0%
E1691
11.0%
A1629
10.6%
T1397
9.1%
O1309
8.5%
W1156
 
7.5%
R1030
 
6.7%
D820
 
5.3%
S752
 
4.9%
U671
 
4.4%
Other values (9)3109
20.2%
ValueCountFrequency (%)
886
100.0%
ValueCountFrequency (%)
/140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15414
93.8%
Common1026
 
6.2%

Most frequent character per script

ValueCountFrequency (%)
N1850
12.0%
E1691
11.0%
A1629
10.6%
T1397
9.1%
O1309
8.5%
W1156
 
7.5%
R1030
 
6.7%
D820
 
5.3%
S752
 
4.9%
U671
 
4.4%
Other values (9)3109
20.2%
ValueCountFrequency (%)
886
86.4%
/140
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII16440
100.0%

Most frequent character per block

ValueCountFrequency (%)
N1850
11.3%
E1691
10.3%
A1629
9.9%
T1397
 
8.5%
O1309
 
8.0%
W1156
 
7.0%
R1030
 
6.3%
886
 
5.4%
D820
 
5.0%
S752
 
4.6%
Other values (11)3920
23.8%

Latitude
Real number (ℝ≥0)

Distinct1465
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.61596899
Minimum47.50943452
Maximum47.73381054
Zeros0
Zeros (%)0.0%
Memory size12.1 KiB
2021-02-23T16:17:48.526162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum47.50943452
5-th percentile47.5391732
Q147.58787647
median47.61229181
Q347.6477973
95-th percentile47.70792077
Maximum47.73381054
Range0.22437602
Interquartile range (IQR)0.05992083

Descriptive statistics

Standard deviation0.04669993476
Coefficient of variation (CV)0.0009807620376
Kurtosis-0.0442789242
Mean47.61596899
Median Absolute Deviation (MAD)0.02755594
Skewness0.2878549453
Sum72804.81658
Variance0.002180883907
MonotocityNot monotonic
2021-02-23T16:17:48.698660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.525932095
 
0.3%
47.663757285
 
0.3%
47.58290494
 
0.3%
47.608315754
 
0.3%
47.521317414
 
0.3%
47.603876573
 
0.2%
47.621240833
 
0.2%
47.641712143
 
0.2%
47.56162263
 
0.2%
47.53095833
 
0.2%
Other values (1455)1492
97.6%
ValueCountFrequency (%)
47.509434521
0.1%
47.509913481
0.1%
47.51030681
0.1%
47.51060341
0.1%
47.510638481
0.1%
47.510812151
0.1%
47.510895461
0.1%
47.511842471
0.1%
47.512761311
0.1%
47.512777391
0.1%
ValueCountFrequency (%)
47.733810541
0.1%
47.733683411
0.1%
47.733146241
0.1%
47.7318232
0.1%
47.731271841
0.1%
47.729714651
0.1%
47.729694451
0.1%
47.729572861
0.1%
47.728997021
0.1%
47.728423331
0.1%

Longitude
Real number (ℝ)

Distinct1450
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.3336496
Minimum-122.4116616
Maximum-122.2587951
Zeros0
Zeros (%)0.0%
Memory size12.1 KiB
2021-02-23T16:17:48.880848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-122.4116616
5-th percentile-122.37786
Q1-122.3428953
median-122.3332714
Q3-122.3230797
95-th percentile-122.2927134
Maximum-122.2587951
Range0.15286651
Interquartile range (IQR)0.0198156

Descriptive statistics

Standard deviation0.02304426883
Coefficient of variation (CV)-0.0001883722828
Kurtosis1.0374564
Mean-122.3336496
Median Absolute Deviation (MAD)0.00998269
Skewness-0.09642095163
Sum-187048.1502
Variance0.000531038326
MonotocityNot monotonic
2021-02-23T16:17:49.063013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.33084025
 
0.3%
-122.30021685
 
0.3%
-122.36689744
 
0.3%
-122.33544854
 
0.3%
-122.32289944
 
0.3%
-122.33369454
 
0.3%
-122.3337863
 
0.2%
-122.35343223
 
0.2%
-122.33374333
 
0.2%
-122.33863033
 
0.2%
Other values (1440)1491
97.5%
ValueCountFrequency (%)
-122.41166161
0.1%
-122.40842541
0.1%
-122.40777511
0.1%
-122.40770071
0.1%
-122.40335991
0.1%
-122.39999782
0.1%
-122.39906241
0.1%
-122.39739451
0.1%
-122.39623371
0.1%
-122.39415071
0.1%
ValueCountFrequency (%)
-122.25879511
0.1%
-122.26175961
0.1%
-122.26238991
0.1%
-122.26265081
0.1%
-122.26294641
0.1%
-122.26417021
0.1%
-122.26576731
0.1%
-122.26666461
0.1%
-122.26813941
0.1%
-122.2685511
0.1%

ZipCode
Real number (ℝ≥0)

Distinct28
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98116.1465
Minimum98101
Maximum98199
Zeros0
Zeros (%)0.0%
Memory size12.1 KiB
2021-02-23T16:17:49.252184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum98101
5-th percentile98101
Q198104
median98109
Q398122
95-th percentile98134
Maximum98199
Range98
Interquartile range (IQR)18

Descriptive statistics

Standard deviation15.48591625
Coefficient of variation (CV)0.0001578324954
Kurtosis8.008767462
Mean98116.1465
Median Absolute Deviation (MAD)8
Skewness2.138777389
Sum150019588
Variance239.813602
MonotocityNot monotonic
2021-02-23T16:17:49.420337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
98134194
12.7%
98104165
10.8%
98101149
 
9.7%
98109135
 
8.8%
98108110
 
7.2%
9812283
 
5.4%
9810582
 
5.4%
9812176
 
5.0%
9811963
 
4.1%
9810361
 
4.0%
Other values (18)411
26.9%
ValueCountFrequency (%)
98101149
9.7%
9810230
 
2.0%
9810361
 
4.0%
98104165
10.8%
9810582
5.4%
9810623
 
1.5%
9810751
 
3.3%
98108110
7.2%
98109135
8.8%
9811215
 
1.0%
ValueCountFrequency (%)
9819918
 
1.2%
981782
 
0.1%
981771
 
0.1%
981552
 
0.1%
981462
 
0.1%
9814441
 
2.7%
981363
 
0.2%
98134194
12.7%
9813351
 
3.3%
9812621
 
1.4%

Interactions

2021-02-23T16:17:17.537815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:17.729990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:17.900144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:18.086314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:18.366569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:18.541728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:18.726896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:18.912065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:19.091744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:19.257895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:19.446065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:19.614219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:19.783372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:19.949532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:20.136703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:20.303846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:20.476002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:20.654164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:20.832325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:21.004482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:21.177639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:21.360806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:21.529960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:21.696110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:21.862262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:22.045428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:22.219587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:22.386747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:22.565902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:22.740060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:22.906210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:23.068358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:23.249522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:23.414681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:23.702935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:23.887102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:24.071269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:24.263444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:24.450623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:24.644799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:24.835965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:25.029140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:25.212306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:25.416492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:25.603671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:25.772816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:25.937966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:26.105118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:26.292288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:26.468448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:26.646619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:26.819767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:26.991924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:27.153070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:27.333236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:27.500892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:27.666042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:27.829190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:27.991337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:28.170509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:28.337661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:28.513813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:28.684968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:28.853121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:29.009454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:29.188177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:29.352326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:29.534501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:29.710651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:29.885820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:30.210106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:30.390270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-23T16:17:30.933763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:31.106921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:31.300096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:31.479259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:31.649413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:31.823572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:31.997731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:32.188904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:32.361061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:32.531215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:32.712380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:32.887548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:33.053690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:33.239859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:33.414018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:33.586174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:33.758330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:33.928485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:34.118667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:34.292825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:34.464973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:34.647138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:34.824299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:34.991451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:35.176628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:35.347775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:35.508922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:35.667065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:35.826210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:36.002370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:36.164518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:36.323662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:36.499823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:36.665973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:36.828120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:37.000792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:37.158927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:37.344095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:37.529264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:37.711429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:38.078763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:38.262930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:38.446097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:38.639652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:38.827842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:39.017014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:39.195175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:39.379343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:39.544502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:39.708651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:39.873801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:40.055958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:40.224111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:40.390262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:40.566422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:40.739588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:40.909743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-23T16:17:41.071882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-02-23T16:17:49.589500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-23T16:17:49.953832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-23T16:17:50.312148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-23T16:17:50.676488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-23T16:17:51.010783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-23T16:17:41.407186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-23T16:17:41.939671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-23T16:17:42.254957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-23T16:17:42.461144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

OSEBuildingIDDataYearPrimaryPropertyTypeYearBuiltNumberofBuildingsNumberofFloorsPropertyGFABuilding(s)PropertyGFAParkingLargestPropertyUseTypeENERGYSTARScoreSiteEnergyUse(kBtu)TotalGHGEmissionsLocationCouncilDistrictCodeNeighborhoodLatitudeLongitudeZipCode
012015Hotel1927112.0884340Hotel65.06981428.0249.43{'latitude': '47.61219025', 'longitude': '-122.33799744', 'human_address': '{"address": "405 OLIVE WAY", "city": "SEATTLE", "state": "WA", "zip": "98101"}'}7DOWNTOWN47.612190-122.33799798101.0
122015Hotel1996111.08850215064Hotel51.08354235.0263.51{'latitude': '47.61310583', 'longitude': '-122.33335756', 'human_address': '{"address": "724 PINE ST", "city": "SEATTLE", "state": "WA", "zip": "98101"}'}7DOWNTOWN47.613106-122.33335898101.0
232015Hotel1969141.09619900Hotel18.073130656.02061.48{'latitude': '47.61334897', 'longitude': '-122.33769944', 'human_address': '{"address": "1900 5TH AVE", "city": "SEATTLE", "state": "WA", "zip": "98101"}'}7DOWNTOWN47.613349-122.33769998101.0
352015Hotel1926110.0613200Hotel1.028229320.01936.34{'latitude': '47.61421585', 'longitude': '-122.33660889', 'human_address': '{"address": "620 STEWART ST", "city": "SEATTLE", "state": "WA", "zip": "98101"}'}7DOWNTOWN47.614216-122.33660998101.0
482015Hotel1980118.010743012460Hotel67.014829099.0507.70{'latitude': '47.6137544', 'longitude': '-122.3409238', 'human_address': '{"address": "401 LENORA ST", "city": "SEATTLE", "state": "WA", "zip": "98121"}'}7DOWNTOWN47.613754-122.34092498121.0
592015Other199912.06009037198Police StationNaN12051984.0304.62{'latitude': '47.6164389', 'longitude': '-122.33676431', 'human_address': '{"address": "810 VIRGINIA ST", "city": "SEATTLE", "state": "WA", "zip": "98101"}'}7DOWNTOWN47.616439-122.33676498101.0
6102015Hotel1926111.0830080Hotel25.06252842.0208.46{'latitude': '47.6141141', 'longitude': '-122.33274086', 'human_address': '{"address": "1619 9TH AVE", "city": "SEATTLE", "state": "WA", "zip": "98101"}'}7DOWNTOWN47.614114-122.33274198101.0
7112015Other192618.01027610Other - Entertainment/Public AssemblyNaN6426022.0199.99{'latitude': '47.61290234', 'longitude': '-122.33130949', 'human_address': '{"address": "901 PINE ST", "city": "SEATTLE", "state": "WA", "zip": "98101"}'}7DOWNTOWN47.612902-122.33130998101.0
8122015Hotel1904115.01639840Hotel46.012633744.0331.61{'latitude': '47.60258934', 'longitude': '-122.33255325', 'human_address': '{"address": "612 2ND AVE", "city": "SEATTLE", "state": "WA", "zip": "98104"}'}7DOWNTOWN47.602589-122.33255398104.0
9152015Hotel1969111.013388419279NaN48.014719853.0576.63{'latitude': '47.60712147', 'longitude': '-122.33431932', 'human_address': '{"address": "1101 4TH AVE", "city": "SEATTLE", "state": "WA", "zip": "98101"}'}7DOWNTOWN47.607121-122.33431998101.0

Last rows

OSEBuildingIDDataYearPrimaryPropertyTypeYearBuiltNumberofBuildingsNumberofFloorsPropertyGFABuilding(s)PropertyGFAParkingLargestPropertyUseTypeENERGYSTARScoreSiteEnergyUse(kBtu)TotalGHGEmissionsLocationCouncilDistrictCodeNeighborhoodLatitudeLongitudeZipCode
1519499262015College/University192513.04283470College/UniversityNaN36367960.01253.31{'latitude': '47.61676902', 'longitude': '-122.3215492', 'human_address': '{"address": "1701 BROADWAY", "city": "SEATTLE", "state": "WA", "zip": "98122"}'}3EAST47.616769-122.32154998122.0
1520499402015Hospital192018.03744660NaN97.078652064.03894.01{'latitude': '47.60984009', 'longitude': '-122.3274412', 'human_address': '{"address": "925 SENECA ST", "city": "SEATTLE", "state": "WA", "zip": "98101"}'}3EAST47.609840-122.32744198101.0
1521499452015Senior Care Community198913.01673000Senior Care CommunityNaN3681105.070.38{'latitude': '47.60895084', 'longitude': '-122.3421375', 'human_address': '{"address": "1531 WESTERN AVE", "city": "SEATTLE", "state": "WA", "zip": "98101"}'}7DOWNTOWN47.608951-122.34213898101.0
1522499462015Supermarket/Grocery Store201018.0411980Supermarket/Grocery Store64.06879291.075.28{'latitude': '47.67057565', 'longitude': '-122.3866853', 'human_address': '{"address": "5700 24TH AVE NW", "city": "SEATTLE", "state": "WA", "zip": "98107"}'}6BALLARD47.670576-122.38668598107.0
1523499582015Other20141NaN209930Repair Services (Vehicle, Shoe, Locksmith, etc)NaN912558.012.28{'latitude': '47.59524558', 'longitude': '-122.3229473', 'human_address': '{"address": "848 7TH AVE S", "city": "SEATTLE", "state": "WA", "zip": "98134"}'}2GREATER DUWAMISH47.595246-122.32294798134.0
1524499662015Other20091NaN402650Pre-school/DaycareNaN1957356.042.40{'latitude': '47.54102707', 'longitude': '-122.31249237', 'human_address': '{"address": "4520 M L KING JR WAY S", "city": "SEATTLE", "state": "WA", "zip": "98108"}'}2SOUTHEAST47.541027-122.31249298108.0
1525499852015Large Office201416.0257986169195Office99.016730779.0210.69{'latitude': '47.6233466', 'longitude': '-122.33968176', 'human_address': '{"address": "500 9TH AVE N", "city": "SEATTLE", "state": "WA", "zip": "98109"}'}7LAKE UNION47.623347-122.33968298109.0
1526499982015Self-Storage Facility\n201414.08757614004Self-Storage FacilityNaN850568.012.40{'latitude': '47.5705386', 'longitude': '-122.2914015', 'human_address': '{"address": "3736 RAINIER AVE S", "city": "SEATTLE", "state": "WA", "zip": "98144"}'}2SOUTHEAST47.570539-122.29140298144.0
1527500022015Other201413.0-5055084198ParkingNaN1389553.09.69{'latitude': '47.66411096', 'longitude': '-122.3166394', 'human_address': '{"address": "4741 11TH AVE NE", "city": "SEATTLE", "state": "WA", "zip": "98105"}'}4NORTHEAST47.664111-122.31663998105.0
1528500382015Mixed Use Property201412.0255320Office84.0628609.04.38{'latitude': '47.66199875', 'longitude': '-122.3867569', 'human_address': '{"address": "2360 W COMMODORE WAY", "city": "SEATTLE", "state": "WA", "zip": "98199"}'}7MAGNOLIA / QUEEN ANNE47.661999-122.38675798199.0